Users' Mental Models of Generative AI Chatbot Ecosystems
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Abstract (arXiv)
The capability of GenAI-based chatbots, such as ChatGPT and Gemini, has expanded quickly in recent years, turning them into GenAI Chatbot Ecosystems. Yet, users' understanding of how such ecosystems work remains unknown. In this paper, we investigate users' mental models of how GenAI Chatbot Ecosystems work. This is an important question because users' mental models guide their behaviors, including making decisions that impact their privacy. Through 21 semi-structured interviews, we uncovered users' four mental models towards first-party (e.g., Google Gemini) and third-party (e.g., ChatGPT) GenAI Chatbot Ecosystems. These mental models centered around the role of the chatbot in the entire ecosystem. We further found that participants held a more consistent and simpler mental model towards third-party ecosystems than the first-party ones, resulting in higher trust and fewer concerns towards the third-party ecosystems. We discuss the design and policy implications based on our results.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2025.
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Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2025The Impact of a Chatbot's Ephemerality-Framing on Self-Disclosure Perceptionscited by 6
- 2026"What Did It Actually Do?": Understanding Risk Awareness and Traceability for Computer-Use Agentscited by 2
- 2026From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistantscited by 1
- 2025Insights into User Interface Innovations from a Design Thinking Workshop at deRSE25cited by 0
- 2026Privacy and Safety Experiences and Concerns of U.S. Women Using Generative AI for Seeking Sexual and Reproductive Health Informationcited by 0
- 2026CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Taskscited by 0
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AIcited by 0
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI19 Apr 2026
- 2026CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Tasks12 Apr 2026
- 2026From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants6 Apr 2026
- 2026"What Did It Actually Do?": Understanding Risk Awareness and Traceability for Computer-Use Agents30 Mar 2026
- 2026Privacy and Safety Experiences and Concerns of U.S. Women Using Generative AI for Seeking Sexual and Reproductive Health Information10 Mar 2026
- 2025Insights into User Interface Innovations from a Design Thinking Workshop at deRSE2526 Aug 2025
- 2025The Impact of a Chatbot's Ephemerality-Framing on Self-Disclosure Perceptions26 May 2025